Python 熊猫：使用groupby/lambda或函数计算加权平均价格？

我有一个数据框，其中4个唯一订单被分成第3-12行。正如您在下面的步骤1、2和3中所看到的，我使用groupby使其成为1 order=1行

然而，我遗漏了一个关键步骤，即计算每个订单的加权平均价格。目前，第2步是计算平均价格

我想做什么：

| 1| Time      | Market    | Type  | Price    | Amount  | Total    | Fee      | Acc     |
| 2|-----------|-----------|-------|----------|---------|----------|----------|---------|
| 3| 22:12:15  | Market 1  | Buy   | 660.33   | 0.0130  | 8.58429  | 0.00085  | MXG_33  |
| 4| 22:12:15  | Market 1  | Buy   | 659.58   | 0.0070  | 4.61706  | 0.00055  | MXG_33  |
| 5| 19:36:08  | Market 1  | Sell  | 670.00   | 0.0082  | 5.49400  | 0.00070  | MXG_33  |
| 6| 19:36:08  | Market 1  | Sell  | 670.33   | 0.0058  | 3.88791  | 0.00048  | MXG_33  |
| 7| 19:36:08  | Market 1  | Sell  | 671.23   | 0.0060  | 4.02738  | 0.00054  | MXG_33  |
| 8| 13:01:41  | Market 1  | Buy   | 667.15   | 0.0015  | 1.00073  | 0.00011  | MXG_33  |
| 9| 13:01:41  | Market 1  | Buy   | 667.10   | 0.0185  | 12.3414  | 0.00132  | MXG_33  |
|10| 07:14:36  | Market 1  | Sell  | 657.55   | 0.0107  | 7.03579  | 0.00079  | MXG_33  |
|11| 07:14:36  | Market 1  | Sell  | 657.08   | 0.0005  | 0.32854  | 0.00004  | MXG_33  |
|12| 07:14:36  | Market 1  | Sell  | 656.59   | 0.0088  | 5.77799  | 0.00071  | MXG_33  |

d_agg = {'Market':'first'
    ,'Type':'first'
    ,'Price':'mean'
    ,'Amount':'sum'
    ,'Total':'sum'
    ,'Fee':'sum'
    ,'Acc':'first'}


(df.groupby('Time', sort=False)['Market','Type','Price','Amount','Total','Fee','Acc'].agg(d_agg).reset_index())

创建一个函数/lambda，用于计算每个订单的加权平均价格（可能基于groupby“Time”列）

---

• 订单1=第3行，第4行
• 订单2=第5、6、7行
• 订单3=第8行，第9行
• 顺序4=第10、11、10行

加权平均价格公式=（（第一价格*金额）+（第二价格*金额））/总金额

订单1的加权平均价格=（660.33*0.0130）+（659.58*0.0070））/0.02=660.06750

步骤1-原始数据帧：

| 1| Time      | Market    | Type  | Price    | Amount  | Total    | Fee      | Acc     |
| 2|-----------|-----------|-------|----------|---------|----------|----------|---------|
| 3| 22:12:15  | Market 1  | Buy   | 660.33   | 0.0130  | 8.58429  | 0.00085  | MXG_33  |
| 4| 22:12:15  | Market 1  | Buy   | 659.58   | 0.0070  | 4.61706  | 0.00055  | MXG_33  |
| 5| 19:36:08  | Market 1  | Sell  | 670.00   | 0.0082  | 5.49400  | 0.00070  | MXG_33  |
| 6| 19:36:08  | Market 1  | Sell  | 670.33   | 0.0058  | 3.88791  | 0.00048  | MXG_33  |
| 7| 19:36:08  | Market 1  | Sell  | 671.23   | 0.0060  | 4.02738  | 0.00054  | MXG_33  |
| 8| 13:01:41  | Market 1  | Buy   | 667.15   | 0.0015  | 1.00073  | 0.00011  | MXG_33  |
| 9| 13:01:41  | Market 1  | Buy   | 667.10   | 0.0185  | 12.3414  | 0.00132  | MXG_33  |
|10| 07:14:36  | Market 1  | Sell  | 657.55   | 0.0107  | 7.03579  | 0.00079  | MXG_33  |
|11| 07:14:36  | Market 1  | Sell  | 657.08   | 0.0005  | 0.32854  | 0.00004  | MXG_33  |
|12| 07:14:36  | Market 1  | Sell  | 656.59   | 0.0088  | 5.77799  | 0.00071  | MXG_33  |

d_agg = {'Market':'first'
    ,'Type':'first'
    ,'Price':'mean'
    ,'Amount':'sum'
    ,'Total':'sum'
    ,'Fee':'sum'
    ,'Acc':'first'}


(df.groupby('Time', sort=False)['Market','Type','Price','Amount','Total','Fee','Acc'].agg(d_agg).reset_index())

步骤2：将订单合并回一行请购单：

| 1| Time      | Market    | Type  | Price    | Amount  | Total    | Fee      | Acc     |
| 2|-----------|-----------|-------|----------|---------|----------|----------|---------|
| 3| 22:12:15  | Market 1  | Buy   | 660.33   | 0.0130  | 8.58429  | 0.00085  | MXG_33  |
| 4| 22:12:15  | Market 1  | Buy   | 659.58   | 0.0070  | 4.61706  | 0.00055  | MXG_33  |
| 5| 19:36:08  | Market 1  | Sell  | 670.00   | 0.0082  | 5.49400  | 0.00070  | MXG_33  |
| 6| 19:36:08  | Market 1  | Sell  | 670.33   | 0.0058  | 3.88791  | 0.00048  | MXG_33  |
| 7| 19:36:08  | Market 1  | Sell  | 671.23   | 0.0060  | 4.02738  | 0.00054  | MXG_33  |
| 8| 13:01:41  | Market 1  | Buy   | 667.15   | 0.0015  | 1.00073  | 0.00011  | MXG_33  |
| 9| 13:01:41  | Market 1  | Buy   | 667.10   | 0.0185  | 12.3414  | 0.00132  | MXG_33  |
|10| 07:14:36  | Market 1  | Sell  | 657.55   | 0.0107  | 7.03579  | 0.00079  | MXG_33  |
|11| 07:14:36  | Market 1  | Sell  | 657.08   | 0.0005  | 0.32854  | 0.00004  | MXG_33  |
|12| 07:14:36  | Market 1  | Sell  | 656.59   | 0.0088  | 5.77799  | 0.00071  | MXG_33  |

d_agg = {'Market':'first'
    ,'Type':'first'
    ,'Price':'mean'
    ,'Amount':'sum'
    ,'Total':'sum'
    ,'Fee':'sum'
    ,'Acc':'first'}


(df.groupby('Time', sort=False)['Market','Type','Price','Amount','Total','Fee','Acc'].agg(d_agg).reset_index())

步骤3-最终结果：（但“价格”列显示的是平均价格，而不是加权平均价格）

---

groupby对象的.apply方法允许您在组级别处理数据并返回数据帧

def fn(group):
    group['weighted_avg'] = group['Price'] * group['Amount'] / group['Amount'].sum()
    return group

d_agg = {'Market':'first'
,'Type':'first'
,'weighted_avg':'sum'
,'Amount':'sum'
,'Total':'sum'
,'Fee':'sum'
,'Acc':'first'}

df.groupby('Time', sort=False).apply(fn).groupby('Time').agg(d_agg)

# if you don't understand what the code is doing, try:
print(df.groupby('Time', sort=False).apply(fn))

---

完美的非常感谢最后一行帮助我理解你是如何做到的，帮助我思考如何解决这些问题。